The present invention relates to search engines which monitor real-time user behavior in order to improve relevance by immediately re-ranking results in response to user actions.
Information Retrieval (IR) systems in general, and search engines in particular, are designed to assist users in locating and identifying documents of interest based on a variety of input regarding the user's information need. This need is most normally received by asking the user to input a query, typically expressed as a set of keywords and sometimes including various Boolean operators. The understanding of the user's intent can then be modified or refined based on other user inputs, both explicit and implicit. The IR system will then comb the universe of available documents for possible matches which, based on any number of different relevancy algorithms, are presented to the user in a descending order of probability of interest. In other words, those documents with the highest probability of being relevant to the user are typically presented first. The objective is to quickly point the user toward documents with the greatest likelihood of satisfying the information need.
Information retrieval on very large data communication networks that contain an enormous amount of information, such as the internet, is particularly difficult given the sizable sets of potentially relevant documents. It is not uncommon for queries on the general internet to return hundreds of thousands, if not millions, of results. Optimally ranking these results based on inherently ambiguous user input, even when taking additional information regarding the user or other searchers into account, is extremely challenging.
IR systems and search engines have thus employed many strategies for combating this issue, most of which seek to achieve a better understanding of the user's information need. Some explicitly request additional information concerning intent, such as directly asking the user to respond to questions regarding both short- and long-terms interests as well as proposing clusters (e.g., a group of key words or categories), drill-downs or reformulations based on the user's query. Others rely on implicit signals derived from user behavior in order to better divine intent.
While the objective of all of these strategies is to produce an optimally ranked set of results based on the user's query and the corpus of user, documents and other information available prior to the user submitting the query, one technique of particular interest involves using real-time user behavior signals to disambiguate intent after the result set has been produced and then exploiting that information to immediately re-rank the results following each user action. (This is described in U.S. patent application Ser. Nos. 11/510,524 entitled “Dynamic search engines results employing user behavior” and 11/743,076 entitled “Real time implicit user modeling for personalized search.”) While the other strategies harness various types of information, both implicit and explicit, to produce a static and frequently sub-optimally ranked set of documents, the results with real-time re-ranking are dynamic as each new user input immediately generates an improved ranking and modified result set.
As an example, a user query for “dolphins” is inherently ambiguous as it is unclear whether the individual's intent refers to the mammal or the football team. By observing the past search behavior of the individual, as well as those of other searchers, many IR strategies will attempt to divine which context has the greater likelihood of satisfying this particular user's information need, at the particular point in time, but it's virtually impossible to know with certainty and so the result set will typically contain a mix of both. Once the user actually selects a result from one context or the other, however, the degree to which the user's intent can be inferred is increased dramatically and the result set can be optimized on the fly by immediately re-ranking the documents. For example, should the user select a result related to the Miami Dolphins football team, documents related to football are “promoted” while simultaneously those related to the mammal are “suppressed.”
While a static result set is easily depicted as a list of documents in something close to descending order of probabilistic relevance, presenting dynamic results poses challenges for the user interface (UI). As the re-ranking of results continuously produces an optimally superior order of documents, it is important for any distraction created by the movement of documents to not impair the advantage of the heightened relevancy.
There is therefore a need to develop a UI capable of delivering the benefits of real-time re-ranking while being as unobtrusive as possible to the user's search experience.